library("RColorBrewer")
library(Signac)
library(Seurat)
library(GenomicRanges)
library(future)
#library(SeuratWrappers)
library(harmony)
library(EnsDb.Hsapiens.v86)
library(stringr)
library(dplyr)
library(ggplot2)
library(patchwork)
library(kableExtra)
library(tidyverse)
set.seed(123)
# Paths
path_to_obj <- ("~/Documents/multiome_tonsil_Lucia/results/R_objects/13.tonsil_multiome_bcells_without_doublets_normalized.rds")
path_to_markers<-("~/Documents/multiome_tonsil_Lucia/results/tables/tonsil_markers_bcell_01.csv")
# Thresholds
max_doublet_score_rna <- 0.3
tonsil_wnn_bcell <- readRDS(path_to_obj)
tonsil_markers_01<-read_csv(path_to_markers)
## New names:
## * `` -> ...1
## Rows: 4679 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): ...1, gene
## dbl (6): p_val, avg_log2FC, pct.1, pct.2, p_val_adj, cluster
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
DimPlot(
tonsil_wnn_bcell,
group.by = "wsnn_res.0.1",
reduction = "wnn.umap",
pt.size = 0.1, label = T
)
Resolution 0.1
top5_tonsil_markers_01<-tonsil_markers_01 %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)
top7_tonsil_markers_01<-tonsil_markers_01 %>% group_by(cluster) %>% top_n(n = 7, wt = avg_log2FC)
top10_tonsil_markers_01<-tonsil_markers_01 %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
df_top5<-as.data.frame(top5_tonsil_markers_01)
kbl(df_top5,caption = "Table of the top 5 marker of each cluster resolution 0.005") %>%
kable_paper("striped", full_width = F)
| …1 | p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | cluster | gene |
|---|---|---|---|---|---|---|---|
| ANK3 | 0 | 1.780595 | 0.549 | 0.239 | 0 | 0 | ANK3 |
| AL355076.2 | 0 | 1.717144 | 0.454 | 0.100 | 0 | 0 | AL355076.2 |
| SSPN | 0 | 1.556627 | 0.360 | 0.109 | 0 | 0 | SSPN |
| TBC1D9 | 0 | 1.386746 | 0.502 | 0.174 | 0 | 0 | TBC1D9 |
| ATXN1 | 0 | 1.373068 | 0.487 | 0.197 | 0 | 0 | ATXN1 |
| COL19A1 | 0 | 2.235729 | 0.844 | 0.288 | 0 | 1 | COL19A1 |
| STEAP1B | 0 | 1.984336 | 0.568 | 0.169 | 0 | 1 | STEAP1B |
| LIX1-AS1 | 0 | 1.642568 | 0.323 | 0.108 | 0 | 1 | LIX1-AS1 |
| ST6GALNAC3 | 0 | 1.466850 | 0.499 | 0.155 | 0 | 1 | ST6GALNAC3 |
| FCER2 | 0 | 1.444418 | 0.464 | 0.124 | 0 | 1 | FCER2 |
| HMGB2 | 0 | 2.937971 | 0.956 | 0.158 | 0 | 2 | HMGB2 |
| TUBA1B | 0 | 2.856285 | 0.968 | 0.243 | 0 | 2 | TUBA1B |
| H2AFZ | 0 | 2.709261 | 0.969 | 0.254 | 0 | 2 | H2AFZ |
| HIST1H4C | 0 | 2.469442 | 0.854 | 0.328 | 0 | 2 | HIST1H4C |
| TOP2A | 0 | 2.453865 | 0.816 | 0.038 | 0 | 2 | TOP2A |
| MAML31 | 0 | 2.641743 | 0.837 | 0.248 | 0 | 3 | MAML3 |
| AC023590.11 | 0 | 2.528443 | 0.986 | 0.297 | 0 | 3 | AC023590.1 |
| AC104170.1 | 0 | 2.441449 | 0.823 | 0.167 | 0 | 3 | AC104170.1 |
| RAPGEF51 | 0 | 2.225608 | 0.932 | 0.255 | 0 | 3 | RAPGEF5 |
| LHFPL21 | 0 | 2.153702 | 0.798 | 0.247 | 0 | 3 | LHFPL2 |
| FYB1 | 0 | 2.493123 | 0.881 | 0.027 | 0 | 4 | FYB1 |
| INPP4B | 0 | 2.431992 | 0.823 | 0.029 | 0 | 4 | INPP4B |
| THEMIS | 0 | 2.035150 | 0.731 | 0.005 | 0 | 4 | THEMIS |
| PRKCH | 0 | 1.878844 | 0.885 | 0.138 | 0 | 4 | PRKCH |
| IL7R | 0 | 1.798465 | 0.680 | 0.012 | 0 | 4 | IL7R |
| IGHGP1 | 0 | 5.711377 | 0.498 | 0.151 | 0 | 5 | IGHGP |
| IGLC11 | 0 | 5.580136 | 0.829 | 0.473 | 0 | 5 | IGLC1 |
| IGKC1 | 0 | 5.655292 | 0.961 | 0.918 | 0 | 5 | IGKC |
| IGHA11 | 0 | 6.011129 | 0.648 | 0.436 | 0 | 5 | IGHA1 |
| IGLC21 | 0 | 5.591442 | 0.894 | 0.761 | 0 | 5 | IGLC2 |
df_top7<-as.data.frame(top7_tonsil_markers_01)
df_mark<-as.data.frame(tonsil_markers_01)
kbl(df_top7,caption = "Table of the top 5 marker of each cluster resolution 0.005") %>%
kable_paper("striped", full_width = F)
| …1 | p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | cluster | gene |
|---|---|---|---|---|---|---|---|
| ANK3 | 0 | 1.780595 | 0.549 | 0.239 | 0 | 0 | ANK3 |
| AL355076.2 | 0 | 1.717144 | 0.454 | 0.100 | 0 | 0 | AL355076.2 |
| SSPN | 0 | 1.556627 | 0.360 | 0.109 | 0 | 0 | SSPN |
| TBC1D9 | 0 | 1.386746 | 0.502 | 0.174 | 0 | 0 | TBC1D9 |
| ATXN1 | 0 | 1.373068 | 0.487 | 0.197 | 0 | 0 | ATXN1 |
| ZDHHC14 | 0 | 1.361222 | 0.677 | 0.407 | 0 | 0 | ZDHHC14 |
| HIPK2 | 0 | 1.356845 | 0.330 | 0.162 | 0 | 0 | HIPK2 |
| COL19A1 | 0 | 2.235729 | 0.844 | 0.288 | 0 | 1 | COL19A1 |
| STEAP1B | 0 | 1.984336 | 0.568 | 0.169 | 0 | 1 | STEAP1B |
| LIX1-AS1 | 0 | 1.642568 | 0.323 | 0.108 | 0 | 1 | LIX1-AS1 |
| ST6GALNAC3 | 0 | 1.466850 | 0.499 | 0.155 | 0 | 1 | ST6GALNAC3 |
| FCER2 | 0 | 1.444418 | 0.464 | 0.124 | 0 | 1 | FCER2 |
| GAB1 | 0 | 1.435708 | 0.349 | 0.130 | 0 | 1 | GAB1 |
| PTPRK | 0 | 1.411378 | 0.516 | 0.168 | 0 | 1 | PTPRK |
| HMGB2 | 0 | 2.937971 | 0.956 | 0.158 | 0 | 2 | HMGB2 |
| TUBA1B | 0 | 2.856285 | 0.968 | 0.243 | 0 | 2 | TUBA1B |
| H2AFZ | 0 | 2.709261 | 0.969 | 0.254 | 0 | 2 | H2AFZ |
| HIST1H4C | 0 | 2.469442 | 0.854 | 0.328 | 0 | 2 | HIST1H4C |
| TOP2A | 0 | 2.453865 | 0.816 | 0.038 | 0 | 2 | TOP2A |
| STMN1 | 0 | 2.439747 | 0.942 | 0.109 | 0 | 2 | STMN1 |
| TUBB | 0 | 2.282930 | 0.949 | 0.197 | 0 | 2 | TUBB |
| MAML31 | 0 | 2.641743 | 0.837 | 0.248 | 0 | 3 | MAML3 |
| AC023590.11 | 0 | 2.528443 | 0.986 | 0.297 | 0 | 3 | AC023590.1 |
| AC104170.1 | 0 | 2.441449 | 0.823 | 0.167 | 0 | 3 | AC104170.1 |
| RAPGEF51 | 0 | 2.225608 | 0.932 | 0.255 | 0 | 3 | RAPGEF5 |
| LHFPL21 | 0 | 2.153702 | 0.798 | 0.247 | 0 | 3 | LHFPL2 |
| AFF21 | 0 | 2.024695 | 0.929 | 0.278 | 0 | 3 | AFF2 |
| FGD61 | 0 | 1.984783 | 0.860 | 0.246 | 0 | 3 | FGD6 |
| FYB1 | 0 | 2.493123 | 0.881 | 0.027 | 0 | 4 | FYB1 |
| INPP4B | 0 | 2.431992 | 0.823 | 0.029 | 0 | 4 | INPP4B |
| THEMIS | 0 | 2.035150 | 0.731 | 0.005 | 0 | 4 | THEMIS |
| PRKCH | 0 | 1.878844 | 0.885 | 0.138 | 0 | 4 | PRKCH |
| IL7R | 0 | 1.798465 | 0.680 | 0.012 | 0 | 4 | IL7R |
| TC2N | 0 | 1.739122 | 0.783 | 0.042 | 0 | 4 | TC2N |
| LEF1 | 0 | 1.735432 | 0.623 | 0.018 | 0 | 4 | LEF1 |
| IGHGP1 | 0 | 5.711377 | 0.498 | 0.151 | 0 | 5 | IGHGP |
| IGHG31 | 0 | 5.571913 | 0.696 | 0.327 | 0 | 5 | IGHG3 |
| IGLC11 | 0 | 5.580136 | 0.829 | 0.473 | 0 | 5 | IGLC1 |
| IGKC1 | 0 | 5.655292 | 0.961 | 0.918 | 0 | 5 | IGKC |
| IGHA11 | 0 | 6.011129 | 0.648 | 0.436 | 0 | 5 | IGHA1 |
| IGLC21 | 0 | 5.591442 | 0.894 | 0.761 | 0 | 5 | IGLC2 |
| IGLC31 | 0 | 5.518655 | 0.721 | 0.603 | 0 | 5 | IGLC3 |
#install.packages("htmlwidgets", type = "binary")
#install.packages("DT", type = "binary")
DT::datatable(df_top7)
DT::datatable(df_mark)
markerGenes <- unique(tonsil_markers_01$gene)
geneSym <- ifelse(test = !grepl('NA', markerGenes),
yes = sub('.*?-', '', markerGenes),
no = sub('-.*', '', markerGenes))
dot.10 <- DotPlot(tonsil_wnn_bcell, features = unique(top10_tonsil_markers_01$gene),cols = 'RdBu', cluster.idents = T) + theme(axis.text.x = element_text( size = 10, vjust = 0.8, hjust = 0.8)) + scale_x_discrete(labels= geneSym)+ggtitle("res 0.1 top 10 of each cluster")
dot.5 <- DotPlot(tonsil_wnn_bcell, features = unique(top5_tonsil_markers_01$gene),cols = 'RdBu', cluster.idents = T) + theme(axis.text.x = element_text( size = 10, vjust = 0.8, hjust = 0.8)) + scale_x_discrete(labels= geneSym)+ggtitle("res 0.1 top 5 of each cluster")
dot.10 +
coord_flip() +
theme(axis.title = element_blank(), axis.text.y = element_text(size = 5))
dot.5 +
coord_flip() +
theme(axis.title = element_blank(), axis.text.y = element_text(size = 7))
top7mark_cluster0<-top7_tonsil_markers_01[["gene"]][1:7]
top7mark_cluster1<-top7_tonsil_markers_01[["gene"]][8:14]
top7mark_cluster2<-top7_tonsil_markers_01[["gene"]][15:21]
top7mark_cluster3<-top7_tonsil_markers_01[["gene"]][22:28]
top7mark_cluster4<-top7_tonsil_markers_01[["gene"]][29:35]
top7mark_cluster5<-top7_tonsil_markers_01[["gene"]][36:42]
markers_gg <- function(x){purrr::map(x, function(x) {
p <- FeaturePlot(
tonsil_wnn_bcell,
features = x,
reduction = "wnn.umap",
pt.size = 0.1
)
p
})}
m<-c("PRDM1","XBP1","IRF4","MEF2B","BCL6")
DZ<-c("SUGCT", "CXCR4", "AICDA")
LZ<- c("CD83","BCL2A1")
GC<- c("MEF2B", "BCL6","IRF4")
PC<- c("PRDM1","SLAMF7", "MZB1", "FKBP11")
markers_gg(DZ)
## [[1]]
##
## [[2]]
##
## [[3]]
markers_gg(LZ)
## [[1]]
##
## [[2]]
markers_gg(GC)
## [[1]]
##
## [[2]]
##
## [[3]]
markers_gg(PC)
## [[1]]
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## [[2]]
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## [[3]]
##
## [[4]]
markers_gg(top7mark_cluster0)
## [[1]]
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## [[2]]
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## [[3]]
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## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
naive_markers<-c("CD79A", "CD79B", "BLNK")
memory_markers<-c("CD27")
markers_gg(naive_markers)
## [[1]]
##
## [[2]]
##
## [[3]]
markers_gg(memory_markers)
## [[1]]
markers_gg(c("MS4A1","NT5E"))
## [[1]]
##
## [[2]]
MS4A1:NAIVE-MEMORY B CELL
NT5E: NAIVE B CELL
markers_gg(top7mark_cluster1)
## [[1]]
##
## [[2]]
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## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
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## [[7]]
markers_gg(top7mark_cluster2)
## [[1]]
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## [[3]]
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## [[4]]
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## [[5]]
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## [[6]]
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## [[7]]
markers_gg(top7mark_cluster3)
## [[1]]
##
## [[2]]
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## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
INPP4B: Immune cell enhanced (memory CD4 T-cell)
AOAH: NK GNLY:NK
markers_gg(top7mark_cluster4)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
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## [[5]]
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## [[6]]
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## [[7]]
markers_gg(top7mark_cluster5)
## [[1]]
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## [[2]]
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## [[3]]
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## [[4]]
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## [[5]]
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## [[6]]
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## [[7]]
p <- FeaturePlot(
tonsil_wnn_bcell,
features = c("NFKB1","FOXO1"),
reduction = "wnn.umap",
pt.size = 0.1
)
p
FeaturePlot(
tonsil_wnn_bcell,
features = "percent.mt",
reduction = "wnn.umap",
pt.size = 0.1
)
VlnPlot(tonsil_wnn_bcell, features = "percent.mt", group.by = "wsnn_res.0.1", pt.size=0)
VlnPlot(tonsil_wnn_bcell, features = "percent_ribo", group.by = "wsnn_res.0.1", pt.size=0)
Idents(tonsil_wnn_bcell) <- "wsnn_res.0.05"
cell.num <- table(Idents(tonsil_wnn_bcell))
cell.num
##
## 0 1 2 3 4
## 26883 7186 6602 2102 1860
new.cluster.ids <- c("Naive/MBC", "Naive CD4 T-celL","GC/DZ", "GC/LZ", "NK T-cell", "PC", "Monocytes","NI")
names(new.cluster.ids) <- levels(tonsil_wnn_bcell)
tonsil_wnn_bcell <- RenameIdents(tonsil_wnn_bcell, new.cluster.ids)
DimPlot(tonsil_wnn_bcell, reduction = "wnn.umap", label = TRUE, pt.size = 0.5)
MARKERS
Immature B cells express CD19, CD 20, CD34, CD38, and CD45R, T-cell receptor/CD3 complex (TCR/CD3 complex)
canonical_bcell_markers <-c("CD34", "CD38", "CD19")
monocytes_markers<-c("LYZ","S100A8")
naive_markers<-c("CD79A", "CD79B", "BLNK")
bib_Bcell_markers<-c("CD19","CR2","MS4A1","RALGPS2","CD79A")
bib_Tcell_markers<-c("CD3E","CD4","CD8A","FOXP3","IL17A")
markers_gg(naive_markers)
## [[1]]
##
## [[2]]
##
## [[3]]
markers_gg(bib_Bcell_markers)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
CD8+ T cell markers:“CD3D”, “CD8A” NK cell markers:“GNLY”, “NKG7”
markers_gg(bib_Tcell_markers)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
tonsil_wnn_bcell <- CellCycleScoring(tonsil_wnn_bcell, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
## Warning: The following features are not present in the object: MLF1IP, not
## searching for symbol synonyms
## Warning: The following features are not present in the object: FAM64A, HN1, not
## searching for symbol synonyms
head(tonsil_wnn_bcell[[]])
## lib_name_barcode orig.ident
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 BCLL_15_T_1_AAACAGCCAGCAACCT-1 SeuratProject
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 BCLL_15_T_1_AAACAGCCAGCTTAGC-1 SeuratProject
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 BCLL_15_T_1_AAACATGCAGGCCAAA-1 SeuratProject
## BCLL_15_T_1_AAACCAACACGAATTT-1 BCLL_15_T_1_AAACCAACACGAATTT-1 SeuratProject
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 BCLL_15_T_1_AAACCGAAGCTATGAC-1 SeuratProject
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 BCLL_15_T_1_AAACCGAAGTAAAGGT-1 SeuratProject
## nCount_RNA nFeature_RNA nCount_ATAC
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 2938 1493 14475
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 5693 2527 14100
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 2377 1121 12678
## BCLL_15_T_1_AAACCAACACGAATTT-1 6476 2543 11978
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 2285 1078 16821
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 5027 2220 15923
## nFeature_ATAC nucleosome_signal
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 6069 0.9178862
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 5960 0.7073955
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 5233 0.5805921
## BCLL_15_T_1_AAACCAACACGAATTT-1 5077 0.5724638
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 6613 0.5307644
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 6633 0.6731493
## nucleosome_percentile TSS.enrichment
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0.88 4.890692
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 0.44 3.685808
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0.15 5.826994
## BCLL_15_T_1_AAACCAACACGAATTT-1 0.14 5.295245
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 0.08 5.239743
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 0.35 4.178657
## TSS.percentile tss.level percent.mt percent_ribo
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0.31 High 9.326072 5.616065
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 0.03 High 3.249605 4.180573
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0.73 High 13.378208 19.099706
## BCLL_15_T_1_AAACCAACACGAATTT-1 0.50 High 2.424336 2.362569
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 0.48 High 14.529540 15.229759
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 0.09 High 5.390889 2.048936
## nCount_peaks nFeature_peaks library_name
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 7403 6067 BCLL_15_T_1
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 7876 6443 BCLL_15_T_1
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 5989 4857 BCLL_15_T_1
## BCLL_15_T_1_AAACCAACACGAATTT-1 5910 4926 BCLL_15_T_1
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 8042 6239 BCLL_15_T_1
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 8606 6978 BCLL_15_T_1
## donor_id sex age age_group hospital assay
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACCAACACGAATTT-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 BCLL-15-T male 33 young_adult CIMA multiome
## barcodes doublet_scores
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 AAACAGCCAGCAACCT-1 0.020
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 AAACAGCCAGCTTAGC-1 0.024
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 AAACATGCAGGCCAAA-1 0.019
## BCLL_15_T_1_AAACCAACACGAATTT-1 AAACCAACACGAATTT-1 0.015
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 AAACCGAAGCTATGAC-1 0.020
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 AAACCGAAGTAAAGGT-1 0.016
## predicted_doublets peaks.weight RNA.weight
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 FALSE 0.5213808 0.4786192
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 FALSE 0.4637829 0.5362171
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 FALSE 0.5030760 0.4969240
## BCLL_15_T_1_AAACCAACACGAATTT-1 FALSE 0.5155517 0.4844483
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 FALSE 0.5918682 0.4081318
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 FALSE 0.4687815 0.5312185
## wsnn_res.0.005 wsnn_res.0.01 seurat_clusters
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0 0 1
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 2 1 3
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0 0 0
## BCLL_15_T_1_AAACCAACACGAATTT-1 0 0 6
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 0 0 0
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 2 1 3
## sub.cluster_0.25 sub.cluster0_0.5 is_doublet
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0 0_4 FALSE
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 2 2 FALSE
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0 0_0 FALSE
## BCLL_15_T_1_AAACCAACACGAATTT-1 0 0_3 FALSE
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 0 0_0 FALSE
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 2 2 FALSE
## wsnn_res.0.05 wsnn_res.0.75 wsnn_res.0.075
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0 1 0
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 2 4 3
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0 0 1
## BCLL_15_T_1_AAACCAACACGAATTT-1 0 9 0
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 0 0 1
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 2 4 3
## is_tcell sub.cluster2 wsnn_res.0.1 wsnn_res.0.25
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 FALSE 0 0 1
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 FALSE 3 3 3
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 FALSE 0 1 0
## BCLL_15_T_1_AAACCAACACGAATTT-1 FALSE 0 0 6
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 FALSE 0 1 0
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 FALSE 3 3 3
## S.Score G2M.Score Phase old.ident
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 -0.032117648 -0.11812569 G1 0
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 -0.007383767 -0.18545173 G1 2
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 -0.034289719 -0.08897424 G1 0
## BCLL_15_T_1_AAACCAACACGAATTT-1 -0.134934431 -0.14585202 G1 0
## BCLL_15_T_1_AAACCGAAGCTATGAC-1 -0.106464784 -0.13573257 G1 0
## BCLL_15_T_1_AAACCGAAGTAAAGGT-1 -0.143077150 -0.12467942 G1 2
print(x = tonsil_wnn_bcell[["pca"]],
dims = 1:10,
nfeatures = 5)
## PC_ 1
## Positive: MKI67, TOP2A, TPX2, HMGB2, NUSAP1
## Negative: POLD3, POLA1, MCM5, CCNE2, G2E3
## PC_ 2
## Positive: MCM4, CLSPN, HELLS, DTL, PCNA
## Negative: GAS2L3, AURKA, CENPE, HMMR, CDC20
## PC_ 3
## Positive: ANLN, E2F8, RRM2, CDC25C, NDC80
## Negative: CDC20, CCNB2, CKS2, CKS1B, NEK2
## PC_ 4
## Positive: TYMS, FEN1, CKS1B, E2F8, RRM2
## Negative: G2E3, GAS2L3, DTL, POLA1, LBR
## PC_ 5
## Positive: LBR, G2E3, CBX5, SLBP, NCAPD2
## Negative: POLD3, EXO1, CDC6, GAS2L3, DTL
## PC_ 6
## Positive: LBR, CCNE2, SLBP, WDR76, CDCA7
## Negative: G2E3, MCM5, NASP, MCM2, POLD3
## PC_ 7
## Positive: POLD3, G2E3, ANP32E, NCAPD2, LBR
## Negative: MCM5, GAS2L3, MCM2, GINS2, KIF20B
## PC_ 8
## Positive: G2E3, CCNE2, DSCC1, CDC6, NEK2
## Negative: POLD3, MCM5, CKAP5, KIF20B, CBX5
## PC_ 9
## Positive: POLA1, ANP32E, NASP, DTL, TMPO
## Negative: SLBP, CENPA, POLD3, CCNE2, MCM5
## PC_ 10
## Positive: CKAP2, TMPO, NCAPD2, MCM5, DSCC1
## Negative: NASP, ANP32E, KIF20B, CKAP5, SLBP
PCNA: Proliferating cell nuclear antigen
# Visualize the distribution of cell cycle markers across
RidgePlot(tonsil_wnn_bcell, features = c("PCNA", "TOP2A", "MCM6", "MKI67"), ncol = 2)
## Picking joint bandwidth of 0.0824
## Picking joint bandwidth of 0.0756
## Picking joint bandwidth of 0.0616
## Picking joint bandwidth of 0.072
tonsil_wnn_bcell <- RunPCA(tonsil_wnn_bcell, features = c(s.genes, g2m.genes))
## Warning in PrepDR(object = object, features = features, verbose = verbose): The
## following 19 features requested have not been scaled (running reduction without
## them): UNG, PRIM1, UHRF1, MLF1IP, RFC2, RPA2, UBR7, MSH2, RAD51, TIPIN, BLM,
## CASP8AP2, USP1, CHAF1B, FAM64A, HN1, RANGAP1, PSRC1, CTCF
## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.
## PC_ 1
## Positive: MKI67, TOP2A, TPX2, HMGB2, NUSAP1, CENPF, CDK1, CENPE, AURKB, GTSE1
## ANLN, NDC80, TUBB4B, BUB1, KIF11, HMMR, BIRC5, DLGAP5, SMC4, UBE2C
## RRM2, CDCA2, NUF2, ECT2, CDCA3, KIF23, CDCA8, KIF2C, CKAP2L, CCNB2
## Negative: POLD3, POLA1, MCM5, CCNE2, G2E3, SLBP, CDC6, MCM2, EXO1, MCM6
## NASP, DTL, GINS2, DSCC1, GAS2L3, CDC45, CENPA, HELLS, ATAD2, CKAP2
## WDR76, TYMS, NEK2, LBR, CBX5, GMNN, MCM4, FEN1, RAD51AP1, TMPO
## PC_ 2
## Positive: MCM4, CLSPN, HELLS, DTL, PCNA, CDC45, GINS2, CDC6, MCM6, WDR76
## BRIP1, ATAD2, POLA1, EXO1, CCNE2, FEN1, SLBP, MCM5, MCM2, RRM2
## E2F8, GMNN, DSCC1, POLD3, RRM1, NASP, TYMS, CDCA7, RAD51AP1, CBX5
## Negative: GAS2L3, AURKA, CENPE, HMMR, CDC20, UBE2C, NEK2, DLGAP5, CENPF, CENPA
## KIF23, CCNB2, CDCA8, TPX2, BUB1, TOP2A, CDCA3, TTK, HJURP, G2E3
## CKAP2L, GTSE1, CKS2, CKAP2, CDC25C, ECT2, NUF2, CKAP5, KIF2C, TUBB4B
## PC_ 3
## Positive: ANLN, E2F8, RRM2, CDC25C, NDC80, RAD51AP1, KIF11, ECT2, TMPO, BRIP1
## HJURP, CKAP2L, CDCA2, KIF23, EXO1, G2E3, GTSE1, TTK, CDK1, DSCC1
## ATAD2, BUB1, CKAP5, MKI67, TYMS, KIF20B, SMC4, POLA1, RRM1, NUSAP1
## Negative: CDC20, CCNB2, CKS2, CKS1B, NEK2, BIRC5, GINS2, HMGB2, TUBB4B, DTL
## NASP, MCM2, ANP32E, MCM5, MCM4, MCM6, SLBP, CENPF, AURKA, CDCA7
## UBE2C, CDC6, CDCA3, GMNN, HMMR, PCNA, CBX5, CDC45, LBR, CENPA
## PC_ 4
## Positive: TYMS, FEN1, CKS1B, E2F8, RRM2, RRM1, AURKB, PCNA, CDCA3, TUBB4B
## ANP32E, CKS2, RAD51AP1, UBE2C, HMGB2, BIRC5, MCM2, NDC80, MKI67, GTSE1
## CDK1, DSCC1, TOP2A, KIF2C, NCAPD2, HJURP, NUSAP1, GMNN, CDCA8, CKAP2L
## Negative: G2E3, GAS2L3, DTL, POLA1, LBR, KIF20B, CDCA7, BRIP1, CENPA, NEK2
## POLD3, HELLS, MCM6, CKAP2, WDR76, CKAP5, CDC45, EXO1, CDCA2, ECT2
## CDC6, TTK, CENPE, AURKA, CCNB2, SMC4, HMMR, ATAD2, CENPF, CCNE2
## PC_ 5
## Positive: LBR, G2E3, CBX5, SLBP, NCAPD2, CDCA7, WDR76, NASP, ANP32E, TMPO
## SMC4, CKAP5, CKS2, TACC3, HMGB2, MCM5, KIF11, HELLS, POLA1, NUSAP1
## RRM1, MCM2, ATAD2, E2F8, MKI67, RRM2, TPX2, BRIP1, NUF2, KIF20B
## Negative: POLD3, EXO1, CDC6, GAS2L3, DTL, NEK2, CENPA, CDC45, CKAP2, CCNE2
## AURKA, RAD51AP1, TTK, CDC20, MCM4, HMMR, CDK1, KIF2C, DLGAP5, GINS2
## ECT2, CLSPN, MCM6, CENPE, HJURP, UBE2C, CDC25C, KIF23, NDC80, CDCA8
tonsil_wnn_bcell <- RunUMAP(object = tonsil_wnn_bcell,
nn.name = "weighted.nn",
reduction.name = "wnn.umap",
reduction.key = "wnnUMAP_" )
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 14:45:04 UMAP embedding parameters a = 0.9922 b = 1.112
## 14:45:05 Commencing smooth kNN distance calibration using 1 thread
## 14:45:08 Initializing from normalized Laplacian + noise
## 14:45:10 Commencing optimization for 200 epochs, with 1412680 positive edges
## 14:45:44 Optimization finished
DimPlot(tonsil_wnn_bcell,
reduction = "wnn.umap",
pt.size = 0.1, label = T, split.by = "age_group")
DimPlot(tonsil_wnn_bcell,
reduction = "wnn.umap",
pt.size = 0.1, label = T)
tonsil_wnn_bcell@meta.data$Phase<-as.character(tonsil_wnn_bcell@meta.data$Phase.y)
Idents(tonsil_wnn_bcell) <- "Phase.y"
DimPlot(tonsil_wnn_bcell,reduction = "wnn.umap")
tonsil_wnn_bcell@meta.data<- tonsil_wnn_bcell@meta.data %>% relocate("old.ident", .after = last_col())
DimPlot(tonsil_wnn_bcell,reduction = "wnn.umap", group.by = "Phase.y", cols = c("red","blue","yellow"))